ArticleGenetic epidemiology2014
A penalized robust method for identifying gene-environment interactions.
Article in Genetic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
- Robust variable selection methods with Cox model-a selective practical benchmark study.Briefings in bioinformatics · 2024Review
- Locally sparse quantile estimation for a partially functional interaction model.Computational statistics & data analysis · 2023Article
- Unified model-free interaction screening via CV-entropy filter.Computational statistics & data analysis · 2023Article
- Integrative functional linear model for genome-wide association studies with multiple traits.Biostatistics (Oxford, England) · 2022Article
- Gene-environment interaction identification via penalized robust divergence.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection.Frontiers in genetics · 2021Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Identification of gene-environment interactions with marginal penalization.Genetic epidemiology · 2020Article
- Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach.Genomics · 2019Article
- Identifying gene-environment interactions incorporating prior information.Statistics in medicine · 2019Article
- Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis.Genetic epidemiology · 2019Article
- Robust genetic interaction analysis.Briefings in bioinformatics · 2019Review
- Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures.Statistics in medicine · 2018Article
- Robust gene-environment interaction analysis using penalized trimmed regression.Journal of statistical computation and simulation · 2018Article
- Identifying gene-environment interactions for prognosis using a robust approach.Econometrics and statistics · 2017Article
- A penalized robust semiparametric approach for gene-environment interactions.Statistics in medicine · 2015Article
- A selective review of robust variable selection with applications in bioinformatics.Briefings in bioinformatics · 2015Review
- Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model.Statistics in medicine · 2014Article
- Integrative Analysis of "-Omics" Data Using Penalty Functions.Wiley interdisciplinary reviews. Computational statisticsArticle
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Authors and funding
6 authors.
Funding
Abstract
In high-throughput studies, an important objective is to identify gene-environment interactions associated with disease outcomes and phenotypes. Many commonly adopted methods assume specific parametric or semiparametric models, which may be subject to model misspecification. In addition, they usually use significance level as the criterion for selecting important interactions. In this study, we adopt the rank-based estimation, which is much less sensitive to model specification than some of the existing methods and includes several commonly encountered data and models as special cases. Penalization is adopted for the identification of gene-environment interactions. It achieves simultaneous estimation and identification and does not rely on significance level. For computation feasibility, a smoothed rank estimation is further proposed. Simulation shows that under certain scenarios, for example, with contaminated or heavy-tailed data, the proposed method can significantly outperform the existing alternatives with more accurate identification. We analyze a lung cancer prognosis study with gene expression measurements under the AFT (accelerated failure time) model. The proposed method identifies interactions different from those using the alternatives. Some of the identified genes have important implications.
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